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        <title>Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation</title>
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        <namePart type="given">Arjun</namePart>
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    <name type="personal">
        <namePart type="given">Dinesh</namePart>
        <namePart type="family">Vishwakarma</namePart>
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    <name type="personal">
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            <namePart type="given">Tanmoy</namePart>
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    <abstract>Recent works on fake news detection have shown the efficacy of using emotions as a feature or emotions-based features for improved performance. However, the impact of these emotion-guided features for fake news detection in cross-domain settings, where we face the problem of domain shift, is still largely unexplored. In this work, we evaluate the impact of emotion-guided features for cross-domain fake news detection, and further propose an emotion-guided, domain-adaptive approach using adversarial learning. We prove the efficacy of emotion-guided models in cross-domain settings for various combinations of source and target datasets from FakeNewsAMT, Celeb, Politifact and Gossipcop datasets.</abstract>
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%0 Conference Proceedings
%T Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation
%A Choudhry, Arjun
%A Khatri, Inder
%A Chakraborty, Arkajyoti
%A Vishwakarma, Dinesh
%A Prasad, Mukesh
%Y Akhtar, Md. Shad
%Y Chakraborty, Tanmoy
%S Proceedings of the 19th International Conference on Natural Language Processing (ICON)
%D 2022
%8 December
%I Association for Computational Linguistics
%C New Delhi, India
%F choudhry-etal-2022-emotion
%X Recent works on fake news detection have shown the efficacy of using emotions as a feature or emotions-based features for improved performance. However, the impact of these emotion-guided features for fake news detection in cross-domain settings, where we face the problem of domain shift, is still largely unexplored. In this work, we evaluate the impact of emotion-guided features for cross-domain fake news detection, and further propose an emotion-guided, domain-adaptive approach using adversarial learning. We prove the efficacy of emotion-guided models in cross-domain settings for various combinations of source and target datasets from FakeNewsAMT, Celeb, Politifact and Gossipcop datasets.
%U https://aclanthology.org/2022.icon-main.10
%P 75-79

Markdown (Informal)

[Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation](https://aclanthology.org/2022.icon-main.10) (Choudhry et al., ICON 2022)

• Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation (Choudhry et al., ICON 2022)

ACL

• Arjun Choudhry, Inder Khatri, Arkajyoti Chakraborty, Dinesh Vishwakarma, and Mukesh Prasad. 2022. Emotion-guided Cross-domain Fake News Detection using Adversarial Domain Adaptation. In Proceedings of the 19th International Conference on Natural Language Processing (ICON), pages 75–79, New Delhi, India. Association for Computational Linguistics.